10 citations · 30 across the 12 of their papers we have counts for
14 papers · 1 filter
ScenePlacer: Reformulating Scene-Level Object Pose Estimation as Visibility Field Learning
Ken Deng, Jiarui Liu, Guibiao Liao +7
We present ScenePlacer, a framework for scene-level pose estimation of objects generated by state-of-the-art native shape generation models, placing generated 3D objects into scene…
SPOON: Towards Coherent Compositional 3D Scene Generation from Uncalibrated Multi-view Images
Guibiao Liao, Mochu Xiang, Heng Li +6
Compositional 3D scene generation aims to recover complete 3D object shapes and their spatial arrangement from visual observations. Recent image-conditioned 3D generators provide s…
GenHOI: Towards Object-Consistent Hand-Object Interaction with Temporally Balanced and Spatially Selective Object Injection
Xuan Huang, Mochu Xiang, Zhelun Shen +9
Hand-Object Interaction (HOI) remains a core challenge in digital human video synthesis, where models must generate physically plausible contact and preserve object identity across…
MVHOI: Bridge Multi-view Condition to Complex Human-Object Interaction Video Reenactment via 3D Foundation Model
Jinguang Tong, Jinbo Wu, Kaisiyuan Wang +12
Human-Object Interaction (HOI) video reenactment aims to transfer the interaction dynamics of a source video to a novel target object while preserving realistic hand-object coordin…
RnG: A Unified Transformer for Complete 3D Modeling from Partial Observations
Mochu Xiang, Zhelun Shen, Xuesong Li +7
Human perceive the 3D world through 2D observations from limited viewpoints. While recent feed-forward generalizable 3D reconstruction models excel at recovering 3D structures from…
PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction
Jiahui Ren, Mochu Xiang, Jiajun Zhu +1
Wide-baseline panorama reconstruction has emerged as a highly effective and pivotal approach for not only achieving geometric reconstruction of the surrounding 3D environment, but…
SABER: Spatially Consistent 3D Universal Adversarial Objects for BEV Detectors
Aixuan Li, Mochu Xiang, Bosen Hou +3
Adversarial robustness of BEV 3D object detectors is critical for autonomous driving (AD). Existing invasive attacks require altering the target vehicle itself (e.g. attaching patc…
Generative Edge Detection with Stable Diffusion
Caixia Zhou, Yaping Huang, Mochu Xiang +3
Edge detection is typically viewed as a pixel-level classification problem mainly addressed by discriminative methods. Recently, generative edge detection methods, especially diffu…
TAVGBench: Benchmarking Text to Audible-Video Generation
Yuxin Mao, Xuyang Shen, Jing Zhang +5
The Text to Audible-Video Generation (TAVG) task involves generating videos with accompanying audio based on text descriptions. Achieving this requires skillful alignment of both a…
Multimodal Variational Auto-encoder based Audio-Visual Segmentation
Yuxin Mao, Jing Zhang, Mochu Xiang +2
We propose an Explicit Conditional Multimodal Variational Auto-Encoder (ECMVAE) for audio-visual segmentation (AVS), aiming to segment sound sources in the video sequence. Existing…
Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation
Mochu Xiang, Jing Zhang, Nick Barnes +1
Effectively measuring and modeling the reliability of a trained model is essential to the real-world deployment of monocular depth estimation (MDE) models. However, the intrinsic i…
Contrastive Conditional Latent Diffusion for Audio-visual Segmentation
Yuxin Mao, Jing Zhang, Mochu Xiang +4
We propose a contrastive conditional latent diffusion model for audio-visual segmentation (AVS) to thoroughly investigate the impact of audio, where the correlation between audio a…
The Second Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Michaela Trescakova +40
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, includin…
Exploring Depth Contribution for Camouflaged Object Detection
Mochu Xiang, Jing Zhang, Yunqiu Lv +3
Camouflaged object detection (COD) aims to segment camouflaged objects hiding in the environment, which is challenging due to the similar appearance of camouflaged objects and thei…